Evolutionary Multi-Objective Workflow Scheduling in Cloud
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 027-118912-5305
- Type
- D - Journal article
- DOI
-
10.1109/TPDS.2015.2446459
- Title of journal
- Ieee Transactions On Parallel And Distributed Systems
- Article number
- -
- First page
- 1344
- Volume
- 27
- Issue
- 5
- ISSN
- 1045-9219
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2016
- URL
-
http://bura.brunel.ac.uk/handle/2438/12614
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 125
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in this leading journal on distributed and high-performance computing, the paper proposes a novel approach to workflow scheduling using evolutionary multi-objective optimization, which was successfully validated against state‐of‐the‐art QoS scheduling algorithms. This paper has generated significant interests within the international community, being a Highly Cited paper and the 3rd most cited paper among all 1237 papers published in the journal since 2016 according to the Wed of Science.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
